Tunable optical neuromorphic network

ABSTRACT

A reservoir computing neuromorphic network includes an input layer comprising one or more input nodes, a reservoir layer comprising a plurality of reservoir nodes, and an output layer comprising one or more output nodes. A portion of at least one of the input layer, the reservoir layer, and the output layer includes an optically tunable material.

BACKGROUND

This application relates to neuromorphic networks, and in particular, totunable optical neuromorphic networks.

Neuromorphic networks are widely used in pattern recognition andclassification, with many potential applications from fingerprint, iris,and face recognition to target acquisition, etc. The parameters (e.g.,‘synaptic weights’) of the neuromorphic networks are adaptively trainedon a set of patterns during a learning process, following which theneuromorphic network is able to recognize or classify patterns of thesame kind.

A key component of a neuromorphic network is the ‘synapse,’ at whichweight information is stored, typically as a continuous-valued variable.For applications that would benefit from compact, high-performance,low-power, portable neuromorphic network computation, it is desirable tobe able to construct high-density hardware neuromorphic networks havinga large number of synapses (10⁹-10¹⁰ or more). Currently a neuromorphicnetwork is typically realized as a software algorithm implemented on ageneral-purpose computer, although hardware for neuromorphic networksexist.

Neuromorphic networks may be used for three broad types of learning. In“supervised learning” a set of (input, desired output) pairs is providedto the neuromorphic network, one at a time, and a learning algorithmfinds values of the “weights” (the adjustable parameters of theneuromorphic network) that minimize a measure of the difference betweenthe actual and the desired outputs over the training set. If theneuromorphic network has been well trained, it will then process a novel(previously unseen) input to yield an output that is similar to thedesired output for that novel input. That is, the neuromorphic networkwill have learned certain patterns that relate input to desired output,and generalized this learning to novel inputs.

In “unsupervised learning,” a set of inputs (without “desired outputs”)is provided to the neuromorphic network, along with a criterion that theneuromorphic network is to optimize. An example of such a criterion isthat the neuromorphic network is able to compress an input into asmaller amount of information (a “code”) in such a way that the code canbe used to reconstruct the input with minimum average error. Theresulting “auto-encoder” network consists of, in sequence, an inputlayer having a number of neurons or nodes, one or more “hidden” layers,a “code” layer (having relatively few neurons or nodes), one or morehidden layers, and an output layer having the same number of neurons ornodes as the input layer. The entire network is trained as if this werea supervised-learning problem, where the “desired output” is defined tobe identical to the input itself.

In a third type of learning, “reinforcement learning,” a“reward/penalty” value is provided (by an external “teacher”). The“reward/penalty” value depends upon the input and the network's output.This value is used to adjust the weights (and therefore the network'soutputs) so as to increase the average “reward.”

For learning, a solution involves using multilevel programming of eachsynaptic resistance unit, and using the functional capability of thecontrollers to program the synaptic levels, while maintaining verycompact synapse structures (e.g., a PCM element plus one to threetransistors, depending upon a desired configuration). For example, using30 nm technology, a synaptic density of 3.6×10⁹ cm⁻² may be achieved,with 6×10⁴ controllers attached to each x-line and each y-line. Thecontrollers may consist of 10⁴ or more transistors. The energy requiredper synapse per step (i.e., per weight change) is several pico-Joules(pJ). For each presentation of an input to the neuromorphic networkduring learning, the desired weight updates at all the synapses may beperformed in a time on the order of 0.02 seconds. During the recognitionstage (i.e., following synapse training), the energy consumption andrecognition time per image may be reduced.

Neuromorphic network applications may include pattern recognition,classification, and identification of fingerprints, faces, voiceprints,similar portions of text, similar strings of genetic code, etc.; datacompression; prediction of the behavior of a systems; feedback control;estimation of missing data; “cleaning” of noisy data; and functionapproximation or “curve fitting” in high-dimensional spaces.

SUMMARY

A reservoir computing neuromorphic network includes an input layercomprising one or more input nodes, a reservoir layer comprising aplurality of reservoir nodes, and an output layer comprising one or moreoutput nodes. A portion of at least one of the input layer, thereservoir layer, and the output layer includes an optically tunablematerial.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a schematic illustration of a neuromorphic network inaccordance with an embodiment of the present disclosure;

FIG. 2 is a schematic illustration of a hardware configuration of aneuromorphic network in accordance with an embodiment of the presentdisclosure; and

FIG. 3 is a schematic illustration of a cascaded neuromorphic network inaccordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

Detailed illustrative embodiments are disclosed herein. However,specific structural and functional details disclosed herein are merelyrepresentative for purposes of describing example embodiments. Exampleembodiments may, however, be embodied in many alternate forms and shouldnot be construed as limited to only the embodiments set forth herein.

Accordingly, while example embodiments are capable of variousmodifications and alternative forms, embodiments thereof are shown byway of example in the drawings and will herein be described in detail.It should be understood, however, that there is no intent to limitexample embodiments to the particular forms disclosed, but to thecontrary, example embodiments are to cover all modifications,equivalents, and alternatives falling within the scope of exampleembodiments. Like numbers refer to like elements throughout thedescription of the figures.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of example embodiments. Asused herein, the term “and/or” includes any and all combinations of oneor more of the associated listed items.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes,” and/or “including” when usedherein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Hereinafter, example embodiments will be described with reference to theattached drawings.

According to example embodiments, a basic layout of hardware-implementedneuromorphic networks consist of a set of regularly-spaced “x” and “y”lines intersecting at synaptic nodes. A synaptic node may consist of aprogrammable analog or multilevel resistor, which may preferably benon-volatile. In at least one example embodiment, this functionality maybe realized by a phase change material (PCM) device, which, ifnecessary, may be programmed with feedback. The read/write functionalitymay be achieved by the presence of compact controller logic units eachhaving 4-5 or more bits, each attached to an “x” and “y” line runningalong edges of the array of synaptic nodes. The controllers havefunctionalities including analog-to-digital (A/D) inputs,digital-to-analog (D/A) outputs, storage for several numbers each having4-5 or more bits, digital I/O lines, and nonlinear sigmoid-type outputs.

One configuration of a neuromorphic network is an optical reservoircomputing system. In such a system, training may be performedexternally, although such configuration may be used with embodimentsdisclosed herein where the training is performed internally to theneuromorphic network. For example, with reference to FIG. 1, a schematicillustration of a neuromorphic network 100 in accordance with anembodiment is shown.

The neuromorphic network 100 is shown as an optical reservoir computingsystem. The neuromorphic network 100 includes an input layer 102 thatincludes one or more input nodes or input neurons 104. In one exampleembodiment, using external training, the input nodes 104 are configuredto provide random, fixed input weights over a number of inputconnections 106. The input connections 106 are configured to opticallyconnect the input layer 102 with a reservoir layer 108.

The reservoir layer 108 includes a number of reservoir nodes or neurons110. In some embodiments, the reservoir nodes 110 form an opticalneuromorphic computing network. Each of the reservoir nodes 110 isconnected to one or more other reservoir nodes 110 by connections 112.For example, in some embodiments the connections 112 may be arbitraryconnections, random, fixed connections, and/or systematic connections,although other configurations of connections may be employed within thereservoir layer 108 without departing from the scope of the presentdisclosure. The reservoir layer 108 may receive inputs from the inputlayer 102 to perform learning, training, and later processing, and maythen provide outputs therefrom. In some embodiments, the reservoir layer108 may be configured with a large number of randomly interconnectednonlinear reservoir nodes 110 with internal connections and feedbackloops 112.

One or more of the reservoir nodes 110 of the reservoir layer 108 areconnected to one or more output nodes 114 of an output layer 116 throughoutput connections 118. In one example, the output connections 118 maybe configured as trained weights, i.e. they will be adjusted such thatthe output from the reservoir may be controlled by the first outputconnections 118, and a desired result may be achieved at the outputlayer 116.

In some embodiments the weighted sum may be a linear weighting and inother embodiments, the weighting may be non-linear. As a result, theoutput layer is a trained or controlled layer with respect to weightingsuch that the neuromorphic network 100 can compute desired results, evenwith unknown inputs. For example, the neuromorphic network 100 may beconfigured with an output layer that is controlled for weighting thatwhen a known input is supplied at the input layer 102, a known output isgenerated at the output layer 116. Based on a number of trainingprocesses like this, the weighted output connections 118 between thereservoir layer 108 and the output layer 116 may be set, and when anunknown input is supplied to the input layer 102, the output at theoutput layer 116 may be robust, reliable, and accurate.

As such, in sum, the input layer 102 may receive some information to beidentified or classified, the input layer 102 transmits information tothe reservoir layer 108, and the reservoir supplies data or informationthat is identified or classified by the output in the output layer 116.

As described herein, weights or weighting may be an altering ormodification of a transmission through a waveguide. That is, thetransmission of a pulse through a waveguide may be controlled to providea greater or lesser weight based on the training of the network, thushaving certain connections be stronger while others are weaker. Forexample, a waveguide can be used where the transmission is tuned duringtraining. In such an example, the output information may be encoded inthe amplitude of an optical signal in the waveguide. The optical signalcan be superimposed with the signal of other waveguides, and such asystem can encode information about a class in different amplitudes.

As another example, weighting may be achieved in a routing changeprocess. For example, one or more Mach-Zehnder interferometers may beused where the phase of the optical mode in one arm of theinterferometer is shifted. A signal of a waveguide can be transferredfrom one waveguide to another waveguide. Such a configuration may enablerouting between different or multiple connections. As a result, thetraining would result in a routing path of input signals to differentoutput waveguides to achieve the desired outcome during the training.

Those of skill in the art will appreciate that other configurations of aneuromorphic network are possible. For example, a reservoir of aneuromorphic network may be configured with virtual reservoir nodes.Time multiplexing may be used in such configurations. However, in theseconfigurations, traditional training is performed at the output layerand performed externally, i.e., trained weights are set and configuredat the output layer. In operation, the procedure for learning, training,and weighting, begins with analyzing an output for a given inputpattern, such as a known input pattern. Adjustment is made to the outputconnections/nodes, i.e., the output weights, such that a target (known)output is achieved. The adjustment may be completed using externalalgorithms that are configured to modify the weighting at the outputlayer—i.e., weighting of the output connections from specific reservoirnodes to the output nodes such that the correct result is achieved. Forexample, software base training of software weights, using variousfitting algorithms, may be performed. Once training is finished, alloutput layers may be measured, but the software weights are kept ormaintained at the values set during training, thus a consistent resultmay be achieved. However, while the output layer is adjusted bysoftware, the input layer is kept constant and the connections andweighting within the reservoir are fixed—and the only modifiable layeris the output layer.

As provided herein, an optical neuromorphic network is provided thatemploys reservoir computing that has reconfigurable reservoir ornetwork, i.e., an internally weighted and trained neuromorphic network.Further, by adjusting the weights of the various connections in thenetwork, the network may be tuned to operate at different stableoperation points, and thus may be re-configurable to each of the stableoperation points. As described herein, the nodes or neurons of thereservoir layer (or other layers) enable hardware tuning of output,input, and reservoir weights, and do not rely on external softwarealgorithms for providing weighting. That is, in accordance with thepresent disclosure, weighting and training of an optical neuromorphicnetwork may be performed at the hardware level. The training may requirea software algorithm to perform the training, such as providinginformation about how to adjust the hardware weights. The resultingtrained state of the network is encoded non-volatilely in the hardware(i.e., in the nodes and the connections therebetween), rather than beinga software weight that is applied during the computing process.

In some embodiments, this is achieved by constructing the reservoirlayer (and/or the input/output layers) from materials whose opticalproperties can be modified permanently, but changeably, such that achange in one of the nodes and/or layers may be long term and achievedthrough stimuli, resulting in a neuromorphic network that is trained atany level and internally, not relaying on any external software and/oralgorithms. The stimuli may be optical, electrical, thermal, mechanical,magnetic, etc., and may depend on the material of the nodes and/orconnections that are used to form the network. As such, in someembodiments, the internal connections 112 between the reservoir nodes110 within the reservoir 108 may be tunable and/or adjusted based on theapplication of stimuli.

Further, advantageously, embodiments employed herein may enablecascading optical neuromorphic computing. That is, multiple neuromorphicnetworks may be configured in a tiered or cascaded series such thatmultiple levels of classifying or computing may be performed within alarge neuromorphic network formed of multiple blocks, as describedherein.

Embodiments described herein provide a reservoir computing network usingoptical signals, where the input, interconnections, and/or output nodesare built from devices and materials whose optical transmission can bemodified by means of a trigger signal (optical or electrical).Advantageously, such networks can be tailored to have operation pointstuned for different wavelengths, operation points optimized fordifferent tasks, and/or, by weighting the input, interconnection, and/oroutput nodes directly, the output nodes can remain in the opticaldomain, and hence can be used as an input for consecutive networks(i.e., cascaded networks). Furthermore, energy consumption by thereservoir computing networks using optical signals described herein isminimized as compared to a traditional approach whereoptical-to-electrical-to-optical conversion is used for the weightingprocess and for consecutive networks.

In accordance with an embodiment of the present disclosure, a siliconphotonics chip is configured as a reservoir computing network. At eachor part of the nodes in the reservoir, and at each or part of the nodesat the input and/or output, a structure, e.g. comprising waveguides,resonators, or interferometers having variable transmission, will beused. The transmission function of the resonator will be varied as it isbased on materials having a strong electro-optic coefficient or moregenerally on materials whose optical properties show a dependence onoptical or electrical stimuli. Various non-limiting examples arematerials where the real part refractive index of the material can besubstantially modified upon the application of an electrical field(e.g., materials with a strong Pockels effect) and/or where theimaginary part of the refractive index can be modified by an electricfield/current (e.g., materials with a crystalline phase transition: VO2,PCM-materials, etc.). Further, in accordance with some non-limitingembodiments, the change of the optical properties in the material mightbe caused by optical stimuli (e.g., for phase transitions,photorefractive effect, etc.). Those of skill in the art will appreciatethat other materials may be used, other than silicon. For example, III-Vsemi-conductor materials may be used, without departing from the scopeof the present disclosure. As such, any materials that may be used forintegrated photonic chips, including silicon, semi-conductor materials,indium phosphide, etc. may be used for forming a reservoir computingnetwork as described herein.

That is, in operation, the neuromorphic network may have nodes and/orlayers that are configured to be modified and trained merely based onthe application of an optical or electrical signal. The adjustment maybe made such that certain connections within the reservoir or betweenthe reservoir and the input or output layers may be weighted.

Turning to FIG. 2, a non-limiting schematic configuration of aneuromorphic network 200 hardware configuration is shown. Although shownwith a particular geometry, those of skill in the art will appreciatethat the configuration shown in FIG. 2 is merely an example and providedfor illustrative and explanatory purposes. For example, a reservoirand/or the input/output layers may take any geometry including round,rectangular, or even undefined or amorphous shapes. The neuromorphicnetwork 200 includes an input layer 202 formed of a plurality of inputconnections 206 which may be configured as input waveguides. In someembodiments the input connections 206 may be formed from photoniccrystals. A photonic crystal is a periodic optical nanostructure thataffects the motion of photons as the photons pass therethrough, and insome embodiments may be formed in one, two, or three dimensions. Aninput light signal or pulse may be transmitted along the inputconnection 206. When the input layer 202 and/or connections are formedas described herein, specific wavelengths, power levels, electricalpulses, etc. may be used to affect the specific input connections 206that are used as input into a reservoir layer 208.

The input connections 206 are positioned and configured to directoptical pulses toward the reservoir layer 208, which may be formed froma random medium. That is, in some embodiments, the reservoir layer 208may be formed as a medium that is modifiable or changeable in opticalproperties based on stimuli. For example, in one non-limiting embodimentthe reservoir layer 208 may be formed having a photonic crystal-likestructure. Further, in another embodiment, a random arrangement of cellsmay form or comprise the reservoir layer 208. Such a random arrangementenables a scattering of light to different output connections 218 in anoutput layer 216, with weighting also possible in the input and outputconnections 206, 218. As shown, the output layer 216 has an arcuategeometry that surrounds the reservoir layer 208. Those of skill in theart will appreciate that any or all of the various layers may take anygeometry or form, and thus FIG. 2 is merely provided as an illustrativeexample of one configuration and embodiment of the present disclosure.

Because the reservoir layer 208 is formed of a random medium that canhave portions thereof change with respect to optical properties based onstimuli, the neuromorphic network 200 may be configured to be trained.As such, the neuromorphic network 200 may be tunable. For example, inaccordance with a non-limiting embodiment, tuning of non-linearities maybe enabled. In operation, various stimuli, such as feedback stimuli fromvarious nodes within the reservoir layer 208 and/or in the output layer216, may be used to strengthen or weaken the connections between thevarious nodes in the reservoir layer 208 and/or between nodes in thereservoir layer 208 and the output layer 216, that is at the outputconnections 218. This may enable a communication path between the nodesof the reservoir layer to be optimized to be trained to provide adesired output from the neuromorphic network 200.

The output from the reservoir layer 208 may be transmitted along theoutput connections 218 and may provide an output result through theoutput layer 216. In an alternative configuration, the outputconnections 218 may be connected to or configured as an input layer ofanother neuromorphic network having a similar construction andconfiguration as the neuromorphic network 200.

In some embodiments, both the input layer 202 and/or the output layer216 may be formed similar to the reservoir layer 208. That is, thematerial used to form the input layer 202 and/or the output layer 216may be changeable with respect to optical properties. Thus, stimuli maybe used to provide weighting at the input layer 202 and/or the outputlayer 216. As will be appreciated by those of skill in the art, anycombination of changeable layers may be used, as the weighting disclosedherein may achieved at one or more of the layers of a neuromorphicnetwork.

As will be appreciated by those of skill in the art, and noted above,the specific layout and geometry of a neuromorphic network does not needto be as depicted in FIG. 2. In various configuration, the neuromorphicnetwork could be an (arbitrary) shaped interference zone (e.g. aphotonic crystal), with a number of input and output waveguides. Assuch, there is no need or requirement for the semi-circular or arcuateshape shown in FIG. 2, but rather FIG. 2 provides one exampleconfiguration.

As noted, the output layer 216 of the neuromorphic network 200 may beconfigured to be fed into another neuromorphic network similar to theneuromorphic network 200. As such, cascading may be enabled.

For example, with reference to FIG. 3, a cascaded neuromorphic network350 is shown. The cascaded neuromorphic network 350 includes a firstneuromorphic block 352. The first neuromorphic block 352 may beconfigured similar to the neuromorphic network 100 shown in FIG. 1 orthe neuromorphic network 200 of FIG. 2.

The output from the first neuromorphic block 352 may be supplied asinput into one or more second neuromorphic blocks 354. Similarly, theoutput of the second neuromorphic blocks 354 may be supplied as inputinto one or more third neuromorphic blocks 356. As will be appreciatedby those of skill in the art, the cascading may continue for as manyblocks as desired.

Further, because each of the neuromorphic blocks is configured as anindividual neuromorphic network providing reservoir computing asdescribed herein, complete modifiability andself-teaching/self-weighting may be implemented at each layer withineach block and further between each block of the cascaded neuromorphicnetwork 350.

The cascaded neuromorphic network 350 of the present disclosure isenabled because each block of the cascaded neuromorphic network 350 isentirely optical in nature. That is, an optical input is provided and anoptical output is generated. Thus, the optical output of one block canbecome the optical input of a subsequent block in the cascadedneuromorphic network 350. However, the weighting processes may beoptical or electrical in nature.

Moreover, because the system, whether configured as a singleneuromorphic network or a cascaded neuromorphic network, is entirelyoptical in terms of inputs and outputs, the systems may be configured tolearn and tune based on different wavelengths. As such, not only iscascading enabled, but a multiplexed optical neural network may beformed. That is, multiple inputs each having a different wavelength maypass through the input layer, the reservoir layer, and the output layer,with each wavelength having a different path through the nodes of eachlayer, based on the weighting that is applied due to the appliedstimuli. Further, weighting may be performed that is wavelengthsensitive. For example, different wavelengths may experience differenttransmission through the same physical weighting element, and thusweighting may be performed for different wavelengths within a singlephysical waveguide or other part of a neuromorphic network.

In the above described embodiments, various materials may be used toform any of the layers of the neuromorphic network, i.e., the inputlayer, the reservoir layer, and/or the output layer. The materialselected to form one or more of the layers may be an optically tunablematerial. That is, the material may have the optical properties thereofchanged based on a stimulus. When a stimuli is applied, the opticalcharacteristics of the material are changed, but after the stimuli isremoved, the material persists in the changed state. Thus, although thematerial is changeable, it is persistent in a state that is achieved.However, further application of stimuli will again change the opticalstate of the material. This is advantageous so that continuous weightingand/or learning may be performed within the layers of the neuromorphicnetwork.

In some non-limiting embodiments, one or more of the layers orcomponents of a neuromorphic network may be formed from photorefractivematerials (e.g., BaTiO₃, LiNbO₃, Pb[Zr_(x)Ti₁-x]O₃, etc.), nonlinearmaterials (e.g., BaTiO₃, KNbO₃, LiNbO₃, LiIO₃, AIN, Si, etc.), phasechange materials (e.g., TiN, GeSbTe [GST], VO₂), magneto-opticalmaterials (e.g., Garnets, (such as Y₃Fe₅O₁₂, etc.), or any othermaterials that may be changeable to optical signals. These materialsenable stimuli, such as optical, thermal, magnetic, electrical,mechanical, etc., to be applied to the components of the various layerssuch that the optical connections between any two specific nodes of anyof the layers may be weighted to optimize the neuromorphic networkcomputing.

In some embodiments, the triggering of the optical changes within theneuromorphic network layers may be an all-optical input, such asphotorefractive or phase changes based on temperature or high opticalpower densities. That is, optical triggers may be used to modify and/orchange the material of the layers such that optical weighting isperformed and achieved. In other embodiments, the triggering of opticalproperties of the material of the various layers of the neuromorphicnetwork may be made through a trough electrical domain, such asproviding electrical feedback at a detector and then applying anelectrical pulse (such as a voltage) to alter the optical properties ofthe material. As will be appreciated by those of skill in the art, anystimuli may be used, including but not limited to optical, thermal,magnetic, electrical, and mechanical stimuli.

A benefit of the present disclosure includes neuromorphic networkinghaving an output layer, an input layer, and/or a reservoir layer thatincludes programmable weights within the material of the layer (orbetween the layers). Further, in the reservoir layer, tuning of thedynamics within the reservoir is enabled. For example, embodimentsdisclosed herein enable adjustable and/or programmable delay linelengths, variable network and/or routing connections, and/or variablesplitters.

Further, advantageously, embodiments described herein enableneuromorphic network computing without software weighting, but ratherprovides hardware weighting within the various layers of the opticalneuromorphic network. Further, as described above, cascaded neuromorphicnetworks are enabled by keeping all processing, inputs, and outputs inthe optical domain, such that the output of one block may be supplieddirectly to the input of another block of a cascaded neuromorphicnetwork. Moreover, by maintaining all aspects in the optical domain,speed advantages arise. For example, no conversion into the electricaldomain for weighting has to be carried out. Instead, during theweighting process, the signal is kept in the ultra-fast optical domain.It will be appreciated that the process of training may be performedoutside of the optical domain, such as when using electrical stimuli forchanging the weights.

Note that variations may be made on the above example embodiments; theexample embodiments are intended to cover all such modifications,equivalents, and alternatives falling within the scope of the exampleembodiments. For example, many nodes may be configured with synapsesdescribed herein located on communication lines between each pair or anytwo nodes in the group of nodes.

While the invention is described with reference to example embodiments,it will be understood by those skilled in the art that various changesmay be made and equivalence may be substituted for elements thereofwithout departing from the scope of the description. In addition, manymodifications may be made to the teachings herein to adapt to aparticular situation without departing from the scope thereof.Therefore, it is intended that the description and claims not be limitedthe embodiments disclosed for carrying out the above described features,but that the disclosure and description includes all embodiments fallingwith the scope of the appended claims. Moreover, the use of the termsfirst, second, etc. does not denote any order of importance, but ratherthe terms first, second, etc. are used to distinguish one element fromanother.

What is claimed is:
 1. A reservoir computing neuromorphic network, comprising: an input layer comprising one or more input nodes; a reservoir layer comprising a plurality of reservoir nodes; and an output layer comprising one or more output nodes, wherein each of the input layer, the reservoir layer, and the output layer includes an optically tunable material.
 2. The reservoir computing neuromorphic network of claim 1, wherein the input layer, the reservoir layer, and the output layer form a first block, the reservoir computing neuromorphic network further comprising: a second block having a second input layer comprising one or more second input nodes, a second reservoir layer comprising a plurality of second reservoir nodes, and a second output layer comprising one or more second output nodes, wherein at least one of the second input layer, the second reservoir layer, and the second output layer is formed from an optically tunable material, and wherein an input to the second block is an output of the first block.
 3. The reservoir computing neuromorphic network of claim 1, wherein the optically tunable material is a III-V semiconductor material, a photorefractive material, an optically nonlinear material, a phase change material, or a magneto-optical material.
 4. The reservoir computing neuromorphic network of claim 1, wherein the input layer is formed from photonic crystals.
 5. The reservoir computing neuromorphic network of claim 1, wherein the reservoir layer comprises a random medium.
 6. The reservoir computing neuromorphic network of claim 1, wherein the reservoir layer comprises photonic crystal-like structure.
 7. The reservoir computing neuromorphic network of claim 1, wherein the reservoir is formed from an optically tunable material.
 8. The reservoir computing neuromorphic network of claim 1, wherein each reservoir node of the reservoir layer is a tunable node.
 9. A reservoir computing neuromorphic network, comprising: a first block having a first input layer comprising one or more first input nodes, a first reservoir layer comprising a plurality of first reservoir nodes, and a first output layer comprising one or more first output nodes, wherein a portion of at least one of the first input layer, the first reservoir layer, and the first output layer includes an optically tunable material; and a second block having a second input layer comprising one or more second input nodes, a second reservoir layer comprising a plurality of second reservoir nodes, and a second output layer comprising one or more second output nodes, wherein a portion of at least one of the second input layer, the second reservoir layer, and the second output layer includes an optically tunable material, wherein an input to the second block is an output of the first block.
 10. The reservoir computing neuromorphic network of claim 9, wherein each of the first input layer, the first output layer, and the first reservoir layer is formed from an optically tunable material.
 11. The reservoir computing neuromorphic network of claim 9, wherein each of the second input layer, the second output layer, and the second reservoir layer is formed from an optically tunable material.
 12. The reservoir computing neuromorphic network of claim 9, wherein the optically tunable material is a III-V semiconductor material, a photorefractive material, a nonlinear material, a phase change material, or a magneto-optical material.
 13. The reservoir computing neuromorphic network of claim 9, wherein the first input layer and the second input layer are each formed from photonic crystals.
 14. The reservoir computing neuromorphic network of claim 9, wherein the first reservoir layer and the second reservoir layer each comprise a random medium.
 15. The reservoir computing neuromorphic network of claim 9, wherein the first reservoir layer and the second reservoir layer each comprise photonic crystal-like structure.
 16. The reservoir computing neuromorphic network of claim 9, wherein the first reservoir layer and the second reservoir layer each are formed from an optically tunable material.
 17. The reservoir computing neuromorphic network of claim 9, wherein each reservoir node of the first reservoir layer and the second reservoir layer is a tunable node.
 18. The reservoir computing neuromorphic network of claim 9, wherein the first block and the second block form a part of a cascaded reservoir computing neuromorphic network.
 19. The reservoir computing neuromorphic network of claim 9, wherein an output from the first block is an optical signal. 